Strength training tied to better heart health than aerobic

Health Technology, Digital Healthcare

Article | July 14, 2023

Your patients have grown to trust your expertise and recommendations in matters regarding their healthcare. As the sector transitions into a more digital playing field, uninterrupted network connectivity is more than just a bonus; it’s a necessity. While there are many different challenges to completely integrating your practice into the digital world, internet outages are the costliest. Downtime can be caused by various factors, which can compromise patient safety, the faith your team instills in you, and your practice’s reputation and revenue. However, investing in the means to maintain a resilient network lets you maximize your network uptime to optimize resources. We'll look at four different strategies and their benefits for your infrastructure so you can focus on what you do best: providing healthcare excellence to your patients. Strengthening Network Infrastructure The traditional way of doing things may be great for your remedies and techniques. Still, with a growing number of patients and their contextually relevant demands, your network needs to be able to accommodate many different booking requests, increase user activity on your server, and store sensitive patient information. High-speed internet connections enhance your network performance and let you, your team, and your patients make the most of your uninterrupted uptime. Fiber-optic networks, when combined with load balancing and proper segmentation, can diffuse and direct network traffic efficiency and prevent congestion, which prevents downtime due to overload. Implementing Network Monitoring and Management Tools Much like your patients visit your practice to ensure everything is all right with the current state of their health, your network must also receive the same treatment. Identifying and pre-emptively resolving potential issues and vulnerabilities will prevent much more destructive or expensive problems from occurring. Use real-time tools to monitor your bandwidth usage and gain visibility of potential bottlenecks. Tools that offer risk monitoring deliver alerts about critical events that pose a threat to your business continuity. Your IT team will be better equipped to troubleshoot issues promptly and optimize performance. Conducting Regular Network Assessments and Audits Once you have the proper monitoring tools to manage your network topology better, proactive troubleshooting is a great way to spot-check whether your current solution is working as it should. A network audit is much like proactive troubleshooting; you are looking to see if anything could harm the overall system and catch it before it can develop. When auditing a network, the primary focus should be security measures. If patient and confidential data is not secure, the smooth operations of your business are the least of your worries. When conducting an audit, consulting with a network service provider will help identify issues with your protocols, data encryption, and firewall configuration. Establishing Redundancy and Disaster Recovery Plans Backing up private and confidential data is crucial to ensuring that sensitive information is not lost or exposed. Minimizing network downtime can often be achieved by having backup systems that will keep running in the event of an attack or outage. For example, a dedicated Cloud Access Network, power supplies, and switches will go a long way. When creating an internet contingency plan, outline steps and protocols with your team that you will take in the event of a complete failure, including things such as brand reputation management, customer service, and data loss prevention. Looking Forward As the lines between in-person and digital are blurred, navigating the complexities of implementing a robust network is paramount to your business. Strengthening your infrastructure, integrating redundant systems, and conducting regular audits and assessments with the proper monitoring and management tools will help you maximize uptime usage and minimize network downtime. Although overwhelming, working with a reputable network service provider can help you embrace your network topology to remain competitive.

Health Technology, AI

Article | July 18, 2023

COVID-19 has been a catalyst for change, with the diagnostics industry taking centre stage and rising to the challenge of a global pandemic. One of the silver linings of this mammoth task has been the unprecedented time and focus dedicated to finding new technologies and solutions within the sector. The lessons learned from the pandemic now need to be taken forward to improve breast and cervical cancer detection, prevention and treatment across the UK over the coming years. In the more immediate term, the diagnostics industry, alongside public health leaders, faces a daunting backlog as screening programmes for breast and cervical cancer were put on pause for months. These two life-saving tests have been some of the most overlooked during the pandemic and getting back on track with screening is critical as we start to turn the corner. We believe innovation in diagnostics, particularly artificial intelligence guided imaging, is a key tool to tackle delays in breast and cervical cancer diagnosis. The scale of the backlog in missed appointments is vast. In the UK, an estimated 600,000 cervical screening appointments were missed in April and May 2020. And an estimated 986,000 women missed their mammograms, of which an estimated 10,700 could be living with undiagnosed breast cancer. It is clear that hundreds of thousands of women have been affected as COVID-19 resulted in the reprioritisation of healthcare systems and resource allocation. Both cervical and breast cancer screening are well suited for digital technologies and the application of AI, given both require highly trained medical professionals to identify rare, subtle changes visually –a process that can be tedious, time-consuming and error prone. Artificial intelligence and computer vision are technologies which could help to significantly improve this. What does AI mean in this context? Before examining the three specific areas where digitisation and AI can help, it is important to define what we mean by AI. It is the application of AI to medical imaging to help accelerate detection and diagnosis. Digitisation is the vital first step in implementing an AI-driven solution – high quality images demand advanced cloud storage solutions and high resolution. The better the quality of the input, the more effectively trained an AI system will be. The first area where AI-guided imaging can play a role is workflow prioritisation. AI, along with increased screening units and mammographers, has the potential to increase breast cancer screening capacity, by removing the need for review by two radiologists. When used as part of a screening programme, AI could effectively and efficiently highlight the areas that are of particular interest for the reader, in the case of breast screening, or cytotechnologist when considering cervical screening. Based on a comparison with the average time taken to read a breast screening image, with AI 13% less time is needed to read mammogram images, improving the efficiency with which images are reviewed. This time saving could mean that radiologists could read more cases a day and potentially clear the backlog more quickly. For digital cytology for cervical cancer screening, the system is able to evaluate tens of thousands of cells from a single patient in a matter of seconds and present the most relevant diagnostic material to a trained medical professional for the final diagnosis. The job of a cytotechnologist is to build a case based on the cells they see. Utilising these tools, we are finding that cytotechnologists and pathologists are significantly increasing their efficiency without sacrificing accuracy to help alleviate the backlog of cervical screening we are seeing in many countries. Prioritising the most vulnerable patients Another key opportunity is applying AI to risk stratification, as it could help to identify women who are particularly at risk and push them further up the queue for regular screening. Conversely, it would also allow the screening interval for those women at lower risk to be extended, creating a more efficient and targeted breast screening programme. For example, women with dense breast tissue have a greater risk factor than having two immediate family members who have suffered from breast cancer. What’s more, dense breasts make it more difficult to identify cancerous cells in standard mammograms. This means that in some cases cancers will be missed, and in others, women will be unnecessarily recalled for further investigation. A simple way to ensure that those most at risk of developing breast cancer are prioritised for screening and seen more regularly would be to analyse all women on the waiting list with AI-guided breast density software. This would allow clinicians to retrospectively identify those women most at risk and move them to the top of the waiting list for mammograms. In the short term, to help tackle the screening backlog, prior mammograms of women on the waiting list could be analysed using the breast density software, so that women at highest risk could be seen first. Finding new workforce models Being able to pool resources will allow resource to be matched to demand beyond borders. Globally, more than half a million women are diagnosed with cervical cancer each year and the majority of these occur where there is a lack of guidance to conduct the screening programme. The digital transformation of cervical screening can connect populations that desperately need screening to resources where that expertise exists. For example, developing countries in Africa could collect samples from patients and image these locally, but rely on resources in the UK to support the interpretation of the images and diagnoses. Digital diagnostics brings the promise of a ‘taxi-hailing’ type model to cervical cancer screening – connecting groups with resources (drivers with cars) to those who are in need (passengers): this is an efficient way of connecting laboratory professionals to doctors and patients around the world. It’s going to take many months to get cancer screening programmes up and running at normal levels again, with continued social distancing measures and additional infection control impacting turnaround times. But diagnostic innovation is on a trajectory that we cannot ignore. It will be key to getting cancer screening programmes get back on track. AI is a fundamental piece of the innovation puzzle and we are proud to be at the forefront of AI solutions for our customers and partners.

Health Technology, Digital Healthcare

Article | September 7, 2023

Embracing the AI Revolution: Transforming Digital Healthcare Software through AI-Enhanced UX Testing The wave of demographic change sweeping the United States presents an urgent call to action for healthcare providers. According to the US Census Bureau, adults over 65 will account for a quarter of the US population by 2060, signaling a drastic shift in healthcare delivery needs. More than half a million of this demographic will be centenarians, accentuating the need for digital experiences tailored to seniors' unique needs. Despite the rapid advancement of digital health technologies, research indicates that many senior citizens struggle to adapt. A recent study reported that 40% of adults over 65 believe their telemedicine visit was inferior to traditional in-person care, with a meager 5% finding it superior. The promise of convenience delivered by digital health is often overshadowed by the frustration associated with technical difficulties. An astounding 75% of senior citizens admit they need assistance when using new electronic devices. Let's consider the patient portal app, a common touchpoint in the digital health journey. Despite its apparent simplicity, seniors find processes like logging in troublesome due to issues like forgotten passwords, technical bugs, or content readability. This scenario underlines the crucial need for comprehensive User Experience (UX) testing to eliminate these barriers and provide a seamless digital health experience. The Complex Landscape of Healthcare UX Testing The complexity of UX testing in healthcare has been exacerbated by the interplay of multiple modules, services, platforms, and vendors. Take Electronic Medical Record (EMR) systems, for instance, which undergo frequent updates, each one potentially impacting the system as a whole. Traditional manual testing methodologies are proving to be time-consuming and costly. Though automation has revolutionized sectors from automotive to finance, the healthcare industry appears to be lagging. A study by the Health Information and Management Systems Society (HIMSS) reveals that a mere 15% of healthcare providers have adopted modern test automation platforms. Meanwhile, a significant 41% still rely on manual testing. As EMR systems grow increasingly complex and customized, this over-reliance on manual testing poses daunting challenges. The gravity of this issue is amplified by a startling revelation from the HIMSS study - only 6% of healthcare executive leaders express confidence in their organizations' testing practices. In an increasingly digitized healthcare environment, such a low level of assurance raises substantial concerns about patient safety. Although 75% of the surveyed providers have invested in software testing to safeguard their bottom lines, nearly two-thirds confess feeling inadequately resourced in terms of time, money, and talent to meet future testing requirements. As the list of testing demands grows, QA teams are frequently stretched thin, leaving many potential user journey scenarios untested. The Power of AI in UX Testing for Better Patient Outcomes AI technologies hold the potential to revolutionize UX testing in healthcare. The modern healthcare application is a labyrinth of potential user journeys - a typical mobile application model can yield over 9 billion separate scenarios. To effectively navigate this colossal testing landscape, test automation tools employing Machine Learning (ML) algorithms are critical. By analyzing historical patterns, prioritized cases, and real-user insights, ML algorithms can auto-generate test cases and meticulously scrutinize each user interaction. This approach ensures an optimal digital experience and robust coverage of potential issues. The HIMSS study also provides a glimmer of hope, revealing that nearly 80% of healthcare providers plan to adopt real-time testing analytics for quality assurance. AI's role becomes pivotal in augmenting the capacity of software testing teams in this scenario. By leveraging historical patterns and prioritizing test cases, ML-powered testing tools can automate crucial tests across various platforms, devices, and operating systems. This symbiosis of human expertise and AI not only bolsters productivity but enables comprehensive testing coverage within tight time constraints. The Future of Healthcare Software UX Testing The path to perfecting a patient’s digital journey is fraught with challenges. Healthcare organizations venturing into automated software testing or contemplating in-house tool replacement must stay abreast of evolving healthcare testing requirements. This understanding is key when evaluating automation vendors against the backdrop of regulatory standards. Opting for a technology-agnostic solution ensures extensive test coverage, boosts efficiency, and guarantees longevity as technologies advance. Introducing your software QA teams to user-friendly, low/no-code test automation tools can simplify the onboarding process and fosters better collaboration with Dev teams and business testers. As we stand at the precipice of this transformative period in healthcare, it's clear that the AI revolution holds the key to unlocking the future of digital healthcare UX testing. By harnessing AI's potential, healthcare providers can ensure a user-friendly, seamless digital experience for the fastest-growing demographic, setting new industry standards in the process.

Article | September 4, 2020

A digital twin is a digital representation of a real-world entity or system. The implementation of a digital twin is a model that mirrors a unique physical object, process, organization, person or other abstraction. For healthcare providers, digital twins provide an abstraction of the healthcare ecosystem’s component characteristics and behaviors. These are used in combination with other real-time health system (RTHS) capabilities to provide real-time monitoring, process simulation for efficiency improvements, population health and long-term, cross-functional statistical analyses. Digital twins have the potential to transform and accelerate decision making, reduce clinical risk, improve operational efficiencies and lower cost of care, resulting in better competitive advantage for HDOs. However, digital twins will only be as valuable as the quality of the data utilized to create them. The digital twin of a real-world entity is a method to create relevance for descriptive data about its modeled entity. How that digital twin is built and used can lead to better-informed care pathways and organizational decisions, but it can also lead clinicians and executives down a path of frustration if they get the source data wrong. The underlying systems that gather and process data are key to the success for digital twin creation. Get those systems right and digital twins can accelerate care delivery and operational efficiencies. Twins in Healthcare Delivery The fact is that HDOs have been using digital twins for years. Although rudimentary in function, digital representations of patients, workflow processes and hospital operations have already been applied by caregivers and administrators across the HDO. For example, a physician uses a digital medical record to develop a treatment plan for a patient. The information in the medical record (a rudimentary digital twin) along with the physician’s experience, training and education combine to provide a diagnostic or treatment plan. Any gaps in information must be compensated through additional data gathering, trial-and-error treatments, intuitive leaps informed through experience or simply guessing. The CIO’s task now is to remove as many of those gaps as possible using available technology to give the physician the greatest opportunity to return their patients to wellness in the most efficient possible manner. Today, one way to close those gaps is to create the technology-based mechanisms to collect accurate data for the various decision contexts within the HDO. These contexts are numerous and include decisioning perspectives for every functional unit within the enterprise. The more accurate the data collected on a specific topic, the higher the value of the downstream digital twin to each decision maker (see Figure 1). Figure 1: Digital Twins Are Only as Good as Their Data Source HDO CIOs and other leaders that base decisions on poor-quality digital twins increase organizational risk and potential patient care risk. Alternatively, high-quality digital twins will accelerate digital business and patient care effectiveness by providing decision makers the best information in the correct context, in the right moment and at the right place — hallmarks of the RTHS. Benefits and Uses Digital Twin Types in Healthcare Delivery Current practices for digital twins take two basic forms: discrete digital twins and composite digital twins. Discrete digital twins are the type that most people think about when approaching the topic. These digital twins are one-dimensional, created from a single set or source of data. An MRI study of a lung, for example, is used to create a digital representation of a patient that can be used by trained analytics processes to detect the subtle image variations that indicate a cancerous tumor. The model of the patient’s lung is a discrete digital twin. There are numerous other examples of discrete digital twins across healthcare delivery, each example tied to data collection technologies for specific clinical diagnostic purposes. Some of these data sources include vitals monitors, imaging technologies for specific conditions, sensors for electroencephalography (EEG) and electrocardiogram (ECG). All these technologies deliver discrete data describing one (or very few) aspects of a patient’s condition. Situational awareness is at the heart of HDO digital twins. They are the culmination of information gathered from IoT and other sources to create an informed, accurate digital model of the real-world healthcare organization. Situational awareness is the engine behind various “hospital of the future,” “digital hospital” and “smart patient room” initiatives. It is at the core of the RTHS. Digital twins, when applied through the RTHS, positively impact these organizational areas (with associated technology examples — the technologies all use one or more types of digital twins to fulfill their capability): Care delivery: Clinical communication and collaboration Next-generation nurse call Alarms and notifications Crisis/emergency management Patient engagement: Experiential wayfinding Integrated patient room Risks Digital Twin Usability Digital twin risk is tied directly to usability. Digital twin usability is another way of looking at the issue created by poor data quality or low data point counts used to create the twins. Decision making is a process that is reliant on inputs from relevant information sources combined with education, experience, risk assessment, defined requirements, criteria and opportunities to reach a plausible conclusion. There is a boundary or threshold that must be reached for each of these inputs before a person or system can derive a decision. When digital twins are used for one or many of these sources, the ability to cross these decision thresholds to create reasonable and actionable conclusions is tied to the accuracy of the twins (see Figure 2). Figure 2: Digital Twin Usability Thresholds For example, the amount of information about a patient room required to decide if the space is too hot or cold is low (due to a single temperature reading from a wall-mounted thermostat). In addition, the accuracy or quality of that data can be low (that is, a few degrees off) and still be effective for deciding to raise or lower the room temperature. To decide if the chiller on the roof of that patient wing needs to be replaced, the decision maker needs much more information. That data may represent all thermostat readings in the wing over a long period of time with some level of verification on temperature accuracy. The data may also include energy load information over the same period consumed by the associated chiller. If viewed in terms of a digital twin, the complexity level and accuracy level of the source data must pass an accuracy threshold that allows users to form accurate decisions. There are multiple thresholds for each digital twin — based on twin quality — whether that twin is a patient, a revenue cycle workflow or hospital wing. These thresholds create a limit of decision impact; the lower the twin quality the less important the available decision for the real-world entity the twin represents. Trusting Digital Twins for HDOs The concept of a limit of detail required to make certain decisions raises certain questions. First, “how does a decision maker know they have enough detail in their digital twin to take action based on what the model is describing about its real-world counterpart?” The answer lies in measurement and monitoring of specific aspects of a digital twin, whether it be a discrete twin, composite twin or organization twin. Users must understand the inputs required for decisions and where twins will provide one or more of the components of that input. They need to examine the required decision criteria in order to reach the appropriate level of expected outcome from the decision itself. These feed into the measurements that users will have to monitor for each twin. These criteria will be unique to each twin. Composite twins will have unique measurements that may be independent from the underlying discrete twin measurement. The monitoring of these key twin characteristics must be as current as the target twin’s data flow or update process. Digital twins that are updated once can have a single measurement to gauge its appropriateness for decisioning. A twin that is updated every second based on event stream data must be measured continuously. This trap is the same for all digital twins regardless of context. The difference is in the potential impact. A facilities decision that leads to cooler-than-desired temperatures in the hallways pales in comparison to a faulty clinical diagnosis that leads to unnecessary testing or negative patient outcomes. All it takes is a single instance of a digital twin used beyond its means with negative results for trust to disappear — erasing the significant investments in time and effort it took to create the twin. That is why it is imperative that twins be considered a technology product that requires constant process improvement. From the IoT edge where data is collected to the data ingestion and analytics processes that consume and mold the data to the digital twin creation routines, all must be under continuous pressure for improvement. Recommendations Include a Concise Digital Twin Vision Within the HDO Digital Transformation Strategy Digital twins are one of the foundational constructs supporting digital transformation efforts by HDO CIOs. They are digital representations of the real-world entities targeted by organizations that benefit from the advances and efficiencies technologies bring to healthcare delivery. Those technology advances and efficiencies will only be delivered successfully if the underlying data and associated digital twins have the appropriate level of precision to sustain the transformation initiatives. To ensure this attention to digital twin worthiness, it is imperative that HDO CIOs include a digital twin vision as part of their organization’s digital transformation strategy. Binding the two within the strategy will reinforce the important role digital twins play in achieving the desired outcomes with all participating stakeholders. Building new capabilities — APIs, artificial intelligence (AI) and other new technologies enable the connections and automation that the platform provides. Leveraging existing systems — Legacy systems that an HDO already owns can be adapted and connected to form part of its digital platform. Applying the platform to the industry — Digital platforms must support specific use cases, and those use cases will reflect the needs of patients, employees and other consumers. Create a Digital Twin Pilot Program Like other advanced technology ideas, a digital twin program is best started as a simple project that can act as a starting point for maturity over time. Begin this by selecting a simple model of a patient, a department or other entity tied to a specific desired business or clinical outcome. The goal is to understand the challenges your organization will face when implementing digital twins. The target for the digital twin should be discrete and easily managed. For example, a digital twin of a blood bank storage facility is a contained entity with a limited number of measurement points, such as temperature, humidity and door activity. The digital twin could be used to simulate the impact of door open time on temperature and humidity within the storage facility. The idea is to pick a project that allows your team to concentrate on data collection and twin creation processes rather than get tied up in specific details of the modeled object. Begin by analyzing the underlying source data required to compose the digital twin, with the understanding that the usability of the twins is directly correlated to its data’s quality. Understand the full data pathway from the IoT devices through to where that data is stored. Think through the data collection type needed for the twin, is discrete data or real-time data required? How much data is needed to form the twin accurately? How accurate is the data generated by the IoT devices? Create a simulation environment to exercise the digital twin through its paces against known operational variables. The twin’s value is tied to how the underlying data represents the response of the modeled entity against external input. Keep this simple to start with — concentrate on the IT mechanisms that create and execute the twin and the simulation environment. Monitor and measure the performance of the digital twin. Use the virtuous cycle to create a constant improvement process for the sample twin. Experience gained through this simple project will create many lessons learned and best practices to follow for complex digital twins that will follow.